Abstract
We present an unsupervised anomaly detection method for hyperspectral imagery (HSI) based on data characteristics inherit in HSI. A locally adaptive technique of iteratively refining the well-known RX detector (LAIRX) is developed. The technique is motivated by the need for better first- and second-order statistic estimation via avoidance of anomaly presence. Overall, experiments show favorable Receiver Operating Characteristic (ROC) curves when compared to a global anomaly detector based upon the Support Vector Data Description (SVDD) algorithm, the conventional RX detector, and decomposed versions of the LAIRX detector. Furthermore, the utilization of parallel and distributed processing allows fast processing time making LAIRX applicable in an operational setting.
Highlights
The RX detector is modified in a preprocessing fashion [1,2,3,4,5,6,7,8,9,10] in order to minimize the false alarm rate while attaining a reasonable true positive rate
We have presented an unsupervised automatic target detection algorithm which builds upon the conventional RX detector by direct manipulation of the RX algorithm
The LAIRX detector must have data preprocessed as principal components before detection which
Summary
The RX detector is modified in a preprocessing fashion [1,2,3,4,5,6,7,8,9,10] in order to minimize the false alarm rate while attaining a reasonable true positive rate. The goal of the SVDD algorithm is to find the hypersphere with minimum volume about a set of random vectors In our case these random vectors are the background pixels with each dimension corresponding to a different spectral band, that is, for a set of pixels, T = {xi, i = 1, . The balance between sample size and number of variables is causing the minimax estimation to converge at a minimum that allows a fairly high Pfa while maintaining an effective characterization of the background spectra. This demonstrates that the bandwidth parameter selection is robust to small sample sizes relative to the number of dimensions or spectral bands
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